Results from Study

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The document summarizes an individual project that examined factors influencing whether higher education institutions offer distance education programs. It looked at 16 predictors using data from 1500 institutions. The model was statistically significant but only explained 2% of variance. Significant predictors were lack of faculty interest, access to instructional resources, and interinstitutional issues. Assumptions of linearity, homoscedasticity, and normality of residuals were violated. Concerns included too many variables and not treating categorical variables correctly.

Education

REF 762: Individual Project Jennifer Styron November 13, 2008 http://ocean. otr . usm .edu/~w668435/ref762/indproject.html

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In this webinar, Prof Bart Rienties will reflect on the process of implementing learning analytics solutions within the UK higher education setting, its implications, and the key lessons learned in the process. The talk will specifically focus on the Open University UK (OU) experience of implementing learning analytics to support its 170k students and 5k staff. Its flagship OU Analyse has been hailed as one of the largest applications of predictive learning analytics at scale for the last five years, making OU one of the leading institutions in learning analytics domain. The talk will reflect on the strong connections between research and practice, educational theory and learning design, scholarship and professional development, and working in multi-disciplinary teams to explain why the OU is at the forefront of implementing learning analytics at scale. At the same time, not all innovations and interventions have worked. During this webinar, Prof Rienties will discuss the lessons learned from implementing learning analytics systems, how learning analytics has been adopted at OU and other UK institutions, and what the implications for higher education might be.

What have we learned from 6 years of implementing learning analytics amongst ...

Bart Rienties

By Professor Bart Rienties, Head of Academic Professional Development, Institute of Educational Technology, The Open University, UK Abstract The Open University UK (OU) has been implementing learning analytics since 2014, starting with one or two modules to its current practice of large-scale implementation across all its 400+ modules and 170.000+ students and 4000+ teaching staff. While a range of reviews (e.g., Adenij, 2019) and scholarly repositories (e.g., Web of Science) indicate that the OU is the largest contributor to academic output in learning analytics in the world, behind the flashy publications and practitioner outputs there are a range of complex issues in terms of ethics and privacy, data infrastructures, buy-in from staff, student engagement, and how to make sense of big data in a complex organisation like the OU. Based upon large-scale big data research we found some interesting tensions in both design and educational theory, such as: – 69% of engagement by students on a week by week basis is determined by how teachers are designing courses (i.e., learning design and instructional design indeed directly influence behaviour and cognition), but many teachers seem reluctant to change their learning design based upon data of what works and what does not work (e.g., making sense of data, agency); – How teachers engage with predictive learning analytics (PLA) significantly improves student outcomes, but only a minority of teachers actually use PLA; – Some disadvantaged groups engage more actively in OU courses, but nonetheless perform lower than non-disadvantaged students. During this CELDA keynote I would like to share some of my own reflections of how the OU has implemented learning analytics, and how these insights are helping towards a stronger evidence-base for data-informed change. Furthermore, by sharing some of the lessons learned from implementing learning analytics on a large scale I hope to provide some dos and don’ts in terms of how you might consider to use data in your own practice and context.

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